SKILLEMALL.ai

DC mrmrmr

(no description)

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: rqth123 v1.0.2 MIT-0 32 files body ≈ 1 561 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 60/100 · Has gaps — weak spots: when it triggers, failures and branches, progress reporting

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
D
50/100
safety, quality, tests
Safety 60%
77
Quality 40%
9
Run on models
none yet
Process rating
C
60/100
Has gaps
When it triggers w 12
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Add a description to the frontmatter: without it the skill never triggers.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

  • high Dangerous commands cmd-pipe-to-shell mrmrmr/README.md:58
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://ollama.com/install.sh | sh
Medium and low: 1
  • medium Exfiltration net-redirectable-api-key run_mragent.py:40
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment

Files scanned: 31. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-missing SKILL.md: frontmatter has no `name`
  • error description-missing SKILL.md: no `description` — the skill can never trigger

Process rating: all ten parameters 60/100

  • 0When it triggers. No condition that starts the skill
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1561 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 0: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 30 items
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 9.

External checks

ClawHub: suspicious
This biomedical analysis skill has a coherent purpose, but it automatically runs generated R code and Python eval on model/API/CSV-derived data with weak scoping and validation.
LLM: suspicious (high) · VirusTotal: · 28 May 2026